Author Archives: Julien Siems

NAS-Bench-301 and the Case for Surrogate NAS Benchmarks

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The Need for Realistic NAS Benchmarks

Neural Architecture Search (NAS) is a logical next step in representation learning as it removes human bias from architecture design, similar to deep learning removing human bias from feature engineering. As such, NAS has experienced rapid growth in recent years, leading to state-of-the-art performance on many tasks. However, empirical evaluations of NAS methods are still problematic. Different NAS papers often use different training pipelines, different search spaces, do not evaluate other methods under comparable settings or cannot afford enough runs for reporting statistical significance. NAS benchmarks attempt to resolve this issue by providing architecture performances on a full search space using a fixed training pipeline without requiring high computational costs. (more…)

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Understanding and Robustifying Differentiable Architecture Search

Optimizing in the search of neural network architectures was initially defined as a discrete problem which intrinsically required to train and evaluate thousands of networks. This of course required huge amount of computational power, which was only possible for few institutions. One-shot neural architecture search (NAS) democratized this tedious search process by training only one large “supergraph” subsuming all possible architectures as its subgraphs. The NAS problem then boils down to finding the optimal path in this big graph, hence reducing the search costs to a small factor of a single function evaluation (one neural network training and evaluation). Notably, Differentiable ARchiTecture Search (DARTS) was widely appreciated in the NAS community due to its conceptual simplicity and effectiveness. In order to relax the discrete space to be continuous, DARTS linearly combines different operation choices to create a mixed operation. Afterwards, one can apply standard gradient descent to optimize in this relaxed architecture space. (more…)

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AutoDispNet: Improving Disparity Estimation with AutoML

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Compared to the state of computer vision 20 years ago, deep learning has enabled more generic methodologies that can be applied to various tasks by automatically extracting meaningful features from the data. However, in practice those methodologies are not as generic as it looks at first glance. While standard neural networks may lead to reasonable solutions, results can be improved significantly by tweaking the details of this design: both the detailed architecture and several further hyperparameters which control the generalization properties of these networks to unseen data. Efficient AutoML in general and neural architecture search (NAS) in particular promise to relieve us from the manual tweaking effort by tuning hyperparameters / architectures to extract those features that maximize the generalization of neural networks. Motivated by the successes of AutoML and NAS for standard image recognition benchmarks, in our ICCV 2019 paper AutoDispNet: Improving Disparity Estimation with AutoML we set out to also apply them to encoder-decoder vision architectures. (more…)

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